Generalized Bayesian model selection for speckle on remote sensing images

Oktay Karakus*, Ercan E. Kuruoglu, Mustafa A. Altinkaya

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
355 Downloads (Pure)


Synthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.

Original languageEnglish
Article number8510857
Pages (from-to)1748-1758
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number4
Early online date26 Oct 2018
Publication statusPublished - 1 Apr 2019


  • envelope distributions
  • generalized (heavy-tailed) Rayleigh distribution
  • Reversible jump MCMC
  • SAR imagery
  • speckle noise modeling
  • ultrasound imagery

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